from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
Daal4py_kmeans_short: 0h 0m 1s
Daal4py_ridge: 0h 0m 2s
Kmeans_short: 0h 0m 3s
Daal4py_logisticregression: 0h 0m 4s
Daal4py_kmeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
Logisticregression: 0h 0m 20s
Kmeans_tall: 0h 0m 24s
Daal4py_kneighborsclassifier_kd_tree: 0h 0m 31s
Kneighborsclassifier_kd_tree: 0h 2m 44s
Daal4py_kneighborsclassifier: 0h 2m 58s
Lightgbm: 0h 5m 0s
Catboost_symmetric: 0h 5m 10s
Histgradientboostingclassifier: 0h 5m 15s
Xgboost: 0h 5m 24s
Catboost: 0h 5m 43s
Kneighborsclassifier: 0h 35m 13s
Total: 1h 9m 19s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config_file_path="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.144 | 0.000 | 5.544 | 0.000 | 1 | 5 | NaN | NaN | 0.516 | 0.000 | 0.280 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.194 | 0.085 | 0.000 | 0.024 | 1 | 5 | 0.830 | 0.942 | 2.132 | 0.029 | 11.349 | 0.160 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.203 | 0.002 | 0.000 | 0.203 | 1 | 5 | 1.000 | 1.000 | 0.086 | 0.000 | 2.369 | 0.021 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.133 | 0.000 | 6.024 | 0.000 | 1 | 1 | NaN | NaN | 0.486 | 0.000 | 0.273 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.275 | 0.057 | 0.000 | 0.013 | 1 | 1 | 0.711 | 0.811 | 2.136 | 0.063 | 6.215 | 0.185 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.002 | 0.000 | 0.187 | 1 | 1 | 1.000 | 1.000 | 0.088 | 0.001 | 2.133 | 0.032 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.136 | 0.000 | 5.884 | 0.000 | 1 | 100 | NaN | NaN | 0.486 | 0.000 | 0.280 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.424 | 0.441 | 0.000 | 0.024 | 1 | 100 | 0.929 | 0.688 | 2.159 | 0.098 | 11.313 | 0.554 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.208 | 0.003 | 0.000 | 0.208 | 1 | 100 | 1.000 | 1.000 | 0.093 | 0.005 | 2.246 | 0.122 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.141 | 0.000 | 5.688 | 0.000 | -1 | 1 | NaN | NaN | 0.497 | 0.000 | 0.283 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.357 | 0.173 | 0.000 | 0.025 | -1 | 1 | 0.711 | 0.942 | 2.142 | 0.070 | 11.838 | 0.393 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.188 | 0.021 | 0.000 | 0.188 | -1 | 1 | 1.000 | 1.000 | 0.088 | 0.003 | 2.135 | 0.255 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.128 | 0.000 | 6.236 | 0.000 | -1 | 5 | NaN | NaN | 0.479 | 0.000 | 0.268 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 36.209 | 0.000 | 0.000 | 0.036 | -1 | 5 | 0.830 | 0.688 | 2.031 | 0.021 | 17.829 | 0.184 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.017 | 0.000 | 0.187 | -1 | 5 | 1.000 | 1.000 | 0.091 | 0.003 | 2.065 | 0.203 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.218 | 0.000 | -1 | 100 | NaN | NaN | 0.490 | 0.000 | 0.263 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.681 | 0.000 | 0.000 | 0.036 | -1 | 100 | 0.929 | 0.811 | 2.104 | 0.096 | 16.959 | 0.775 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.177 | 0.016 | 0.000 | 0.177 | -1 | 100 | 1.000 | 1.000 | 0.093 | 0.012 | 1.898 | 0.299 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.268 | 0.000 | 1 | 5 | NaN | NaN | 0.113 | 0.000 | 0.528 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.525 | 0.137 | 0.000 | 0.022 | 1 | 5 | 0.986 | 0.989 | 0.412 | 0.002 | 52.230 | 0.427 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.001 | 0.000 | 0.024 | 1 | 5 | 1.000 | 1.000 | 0.007 | 0.000 | 3.675 | 0.133 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.265 | 0.000 | 1 | 1 | NaN | NaN | 0.113 | 0.000 | 0.535 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.643 | 0.033 | 0.000 | 0.011 | 1 | 1 | 0.976 | 0.991 | 0.350 | 0.003 | 30.369 | 0.246 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.007 | 0.000 | 2.199 | 0.099 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.263 | 0.000 | 1 | 100 | NaN | NaN | 0.113 | 0.000 | 0.536 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.977 | 0.139 | 0.000 | 0.022 | 1 | 100 | 0.985 | 0.983 | 0.354 | 0.007 | 62.001 | 1.281 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.001 | 0.000 | 0.025 | 1 | 100 | 1.000 | 1.000 | 0.009 | 0.003 | 2.841 | 0.976 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.062 | 0.000 | 0.260 | 0.000 | -1 | 1 | NaN | NaN | 0.115 | 0.000 | 0.538 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 22.017 | 0.097 | 0.000 | 0.022 | -1 | 1 | 0.976 | 0.989 | 0.427 | 0.010 | 51.557 | 1.237 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.002 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.007 | 0.000 | 2.914 | 0.340 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.268 | 0.000 | -1 | 5 | NaN | NaN | 0.114 | 0.000 | 0.521 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.887 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.986 | 0.983 | 0.358 | 0.019 | 91.795 | 4.755 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.002 | 0.000 | 0.030 | -1 | 5 | 1.000 | 1.000 | 0.007 | 0.000 | 4.268 | 0.314 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.268 | 0.000 | -1 | 100 | NaN | NaN | 0.112 | 0.000 | 0.530 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.154 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.985 | 0.991 | 0.359 | 0.018 | 92.352 | 4.627 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.031 | 0.002 | 0.000 | 0.031 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.821 | 0.372 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.227 | 0.000 | 0.025 | 0.000 | -1 | 1 | NaN | NaN | 0.807 | 0.000 | 3.999 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.456 | 0.022 | 0.000 | 0.000 | -1 | 1 | 0.960 | 0.975 | 0.615 | 0.018 | 0.741 | 0.041 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 4.141 | 1.393 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.268 | 0.000 | 0.024 | 0.000 | 1 | 5 | NaN | NaN | 0.797 | 0.000 | 4.101 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.447 | 0.019 | 0.000 | 0.001 | 1 | 5 | 0.969 | 0.974 | 0.196 | 0.004 | 7.387 | 0.167 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 3.825 | 1.630 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.218 | 0.000 | 0.025 | 0.000 | -1 | 5 | NaN | NaN | 0.763 | 0.000 | 4.217 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.851 | 0.033 | 0.000 | 0.001 | -1 | 5 | 0.969 | 0.962 | 0.118 | 0.005 | 7.230 | 0.400 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 16.469 | 7.112 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.256 | 0.000 | 0.025 | 0.000 | 1 | 100 | NaN | NaN | 0.771 | 0.000 | 4.225 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.837 | 0.046 | 0.000 | 0.005 | 1 | 100 | 0.971 | 0.975 | 0.616 | 0.017 | 7.852 | 0.235 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 4.164 | 1.499 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.237 | 0.000 | 0.025 | 0.000 | 1 | 1 | NaN | NaN | 0.769 | 0.000 | 4.209 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.761 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.960 | 0.974 | 0.208 | 0.011 | 3.659 | 0.199 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 3.026 | 1.307 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.254 | 0.000 | 0.025 | 0.000 | -1 | 100 | NaN | NaN | 0.787 | 0.000 | 4.133 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.751 | 0.028 | 0.000 | 0.003 | -1 | 100 | 0.971 | 0.962 | 0.119 | 0.013 | 23.210 | 2.631 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.001 | 0.000 | 0.007 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 25.467 | 9.997 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.883 | 0.000 | 0.018 | 0.000 | -1 | 1 | NaN | NaN | 0.530 | 0.000 | 1.666 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.970 | 0.990 | 0.007 | 0.002 | 3.674 | 0.784 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 17.719 | 12.273 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.873 | 0.000 | 0.018 | 0.000 | 1 | 5 | NaN | NaN | 0.529 | 0.000 | 1.651 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.029 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.978 | 0.987 | 0.001 | 0.000 | 19.177 | 5.914 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 6.025 | 4.499 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.877 | 0.000 | 0.018 | 0.000 | -1 | 5 | NaN | NaN | 0.529 | 0.000 | 1.658 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.029 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.978 | 0.980 | 0.001 | 0.000 | 34.501 | 8.956 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 26.936 | 19.593 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.931 | 0.000 | 0.017 | 0.000 | 1 | 100 | NaN | NaN | 0.550 | 0.000 | 1.691 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.056 | 0.001 | 0.000 | 0.000 | 1 | 100 | 0.981 | 0.990 | 0.008 | 0.000 | 7.167 | 0.408 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.547 | 3.749 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.860 | 0.000 | 0.019 | 0.000 | 1 | 1 | NaN | NaN | 0.525 | 0.000 | 1.640 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.970 | 0.987 | 0.001 | 0.000 | 22.102 | 4.461 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.941 | 4.172 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.870 | 0.000 | 0.018 | 0.000 | -1 | 100 | NaN | NaN | 0.535 | 0.000 | 1.626 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.050 | 0.003 | 0.000 | 0.000 | -1 | 100 | 0.981 | 0.980 | 0.001 | 0.000 | 49.773 | 15.297 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 21.696 | 16.360 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.711 | 0.000 | 0.675 | 0.000 | k-means++ | NaN | 30 | NaN | 0.481 | 0.0 | 1.478 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.281 | 0.000 | k-means++ | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 10.154 | 5.668 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 12.514 | 8.762 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.570 | 0.000 | 0.842 | 0.000 | random | NaN | 30 | NaN | 0.508 | 0.0 | 1.123 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.323 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.526 | 5.511 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.148 | 7.574 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.920 | 0.000 | 3.468 | 0.000 | k-means++ | NaN | 30 | NaN | 2.908 | 0.0 | 2.379 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 12.180 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.812 | 3.309 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.016 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.973 | 6.381 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.286 | 0.000 | 3.818 | 0.000 | random | NaN | 30 | NaN | 3.122 | 0.0 | 2.013 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 13.265 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.212 | 2.628 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.017 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.857 | 5.616 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.272 | 0.000 | 0.012 | 0.000 | k-means++ | NaN | 20 | NaN | 0.037 | 0.0 | 7.454 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.165 | 0.000 | k-means++ | 0.000 | 20 | 0.004 | 0.001 | 0.0 | 2.658 | 0.377 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.704 | 5.572 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.087 | 0.000 | 0.037 | 0.000 | random | NaN | 20 | NaN | 0.096 | 0.0 | 0.910 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.160 | 0.000 | random | 0.003 | 20 | 0.004 | 0.001 | 0.0 | 2.761 | 0.446 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.707 | 6.447 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.716 | 0.000 | 0.223 | 0.000 | k-means++ | NaN | 20 | NaN | 0.154 | 0.0 | 4.643 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.004 | 0.002 | 4.110 | 0.000 | k-means++ | 0.309 | 20 | 0.261 | 0.001 | 0.0 | 3.322 | 1.484 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.011 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.478 | 3.681 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.229 | 0.000 | 0.700 | 0.000 | random | NaN | 20 | NaN | 0.383 | 0.0 | 0.597 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.674 | 0.000 | random | 0.295 | 20 | 0.343 | 0.001 | 0.0 | 2.202 | 0.439 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.711 | 4.608 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.617 | 0.0 | [-0.10156824] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.950 | 0.0 | 5.958 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [45.47105455] | 0.000 | NaN | NaN | NaN | NaN | 0.517 | 0.000 | 0.0 | 0.887 | 0.334 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.21762667] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.363 | 0.349 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [28] | 0.829 | 0.0 | [-2.47938816] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.724 | 0.0 | 1.145 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [28] | 0.002 | 0.0 | [120.02344744] | 0.000 | NaN | NaN | NaN | NaN | 0.220 | 0.003 | 0.0 | 0.594 | 0.112 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [28] | 0.000 | 0.0 | [19.69909629] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.142 | 0.084 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.208 | 0.0 | 0.385 | 0.0 | NaN | NaN | NaN | 0.200 | 0.000 | 1.039 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 7.846 | 0.0 | NaN | NaN | 0.093 | 0.017 | 0.001 | 0.608 | 0.028 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.085 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.651 | 0.549 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.447 | 0.0 | 0.553 | 0.0 | NaN | NaN | NaN | 0.262 | 0.000 | 5.531 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 1.807 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 1.751 | 1.965 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.638 | 0.625 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()